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Purpose: Whole body insulin sensitivity (Si) typically improves after aerobic exercise training; however, individual responses can be highly variable. The purpose of this study was to use global gene expression to identify skeletal muscle genes that correlate with exercise-induced Si changes., Methods: Longitudinal cohorts from the Studies of Targeted Risk Reduction Intervention through Defined Exercise were used as Discovery (Affymetrix) and Confirmation (Illumina) of vastus lateralis gene expression profiles. Discovery (n = 39; 21 men) and Confirmation (n = 42; 19 men) cohorts were matched for age (52 ± 8 vs 51 ± 10 yr), body mass index (30.4 ± 2.8 vs 29.7 ± 2.8 kg·m), and V˙O2max (30.4 ± 2.8 vs 29.7 ± 2.8 mL·kg·min). Si was determined via intravenous glucose tolerance test pretraining and posttraining. Pearson product-moment correlation coefficients determined relationships between a) baseline and b) training-induced changes in gene expression and %ΔSi after training., Results: Expression of 2454 (Discovery) and 1778 genes (Confirmation) at baseline were significantly (P < 0.05) correlated to %ΔSi; 112 genes overlapped. Pathway analyses identified Ca signaling-related transcripts in this 112-gene list. Expression changes of 1384 (Discovery) and 1288 genes (Confirmation) after training were significantly (P < 0.05) correlated to %ΔSi; 33 genes overlapped, representing contractile apparatus of skeletal and smooth muscle genes. Pyruvate dehydrogenase phosphatase regulatory subunit expression at baseline (P = 0.01, r = 0.41) and posttraining (P = 0.01, r = 0.43) were both correlated with %ΔSi., Conclusions: Exercise-induced adaptations in skeletal muscle Si are related to baseline levels of Ca-regulating transcripts, which may prime the muscle for adaptation. Relationships between %ΔSi and pyruvate dehydrogenase phosphatase regulatory, a regulatory subunit of the pyruvate dehydrogenase complex, indicate that the Si response is strongly related to key steps in metabolic regulation., Competing Interests: We report no competing or conflicts of interests. The results of the present study do not constitute endorsement by ACSM.

Background: Innate immune responses are fine-tuned by small noncoding RNA molecules termed microRNAs (miRs) that modify gene expression in response to the environment. During acute infections, miRs can be secreted in extracellular vesicles (EV) to facilitate cell-to-cell genetic communication. The purpose of this study was to characterize the baseline population of miRs secreted in EVs in the airways of young children (airway secretory microRNAome) and examine the changes during rhinovirus (RV) infection, the most common cause of asthma exacerbations and the most important early risk factor for the development of asthma beyond childhood. Methods: Nasal airway secretions were obtained from children (≤3 yrs. old) during PCR-confirmed RV infections (n = 10) and age-matched controls (n = 10). Nasal EVs were isolated with polymer-based precipitation and global miR profiles generated using NanoString microarrays. We validated our in vivo airway secretory miR data in an in vitro airway epithelium model using apical secretions from primary human bronchial epithelial cells (HBEC) differentiated at air-liquid interface (ALI). Bioinformatics tools were used to determine the unified (nasal and bronchial) signature airway secretory miRNAome and changes during RV infection in children. Results: Multiscale analysis identified four signature miRs comprising the baseline airway secretory miRNAome: hsa-miR-630, hsa-miR-302d-3p, hsa- miR-320e, hsa-miR-612. We identified hsa-miR-155 as the main change in the baseline miRNAome during RV infection in young children. We investigated the potential biological relevance of the airway secretion of hsa-mir-155 using in silico models derived from gene datasets of experimental in vivo human RV infection. These analyses confirmed that hsa-miR-155 targetome is an overrepresented pathway in the upper airways of individuals infected with RV. Conclusions: Comparative analysis of the airway secretory microRNAome in children indicates that RV infection is associated with airway secretion of EVs containing miR-155, which is predicted in silico to regulate antiviral immunity. Further characterization of the airway secretory microRNAome during health and disease may lead to completely new strategies to treat and monitor respiratory conditions in all ages. [ABSTRACT FROM AUTHOR]

Background: MicroRNAs (miRNA) are short nucleotides that down-regulate its target genes. Various miRNA target prediction algorithms have used sequence complementarity between miRNA and its targets. Recently, other algorithms tried to improve sequence-based miRNA target prediction by exploiting miRNA-mRNA expression profile data. Some web-based tools are also introduced to help researchers predict targets of miRNAs from miRNA-mRNA expression profile data. A demand for a miRNA-mRNA visual analysis tool that features novel miRNA prediction algorithms and more interactive visualization techniques exists. Results: We designed and implemented miRTarVis, which is an interactive visual analysis tool that predicts targets of miRNAs from miRNA-mRNA expression profile data and visualizes the resulting miRNA-target interaction network. miRTarVis has intuitive interface design in accordance with the analysis procedure of load, filter, predict, and visualize. It predicts targets of miRNA by adopting Bayesian inference and MINE analyses, as well as conventional correlation and mutual information analyses. It visualizes a resulting miRNA-mRNA network in an interactive Treemap, as well as a conventional node-link diagram. miRTarVis is available at http://hcil.snu.ac.kr/~rati/ miRTarVis/index.html. Conclusions: We reported findings from miRNA-mRNA expression profile data of asthma patients using miRTarVis in a case study. miRTarVis helps to predict and understand targets of miRNA from miRNA-mRNA expression profile data. [ABSTRACT FROM AUTHOR]

An important goal of clinical genomics is to be able to estimate the risk of adverse disease outcomes. Between 5% and 10% of individuals with ulcerative colitis (UC) require colectomy within 5 years of diagnosis, but polygenic risk scores (PRSs) utilizing findings from genome-wide association studies (GWASs) are unable to provide meaningful prediction of this adverse status. By contrast, in Crohn disease, gene expression profiling of GWAS-significant genes does provide some stratification of risk of progression to complicated disease in the form of a transcriptional risk score (TRS). Here, we demonstrate that a measured TRS based on bulk rectal gene expression in the PROTECT inception cohort study has a positive predictive value approaching 50% for colectomy. Single-cell profiling demonstrates that the genes are active in multiple diverse cell types from both the epithelial and immune compartments. Expression quantitative trait locus (QTL) analysis identifies genes with differential effects at baseline and week 52 follow-up, but for the most part, differential expression associated with colectomy risk is independent of local genetic regulation. Nevertheless, a predicted polygenic transcriptional risk score (PPTRS) derived by summation of transcriptome-wide association study (TWAS) effects identifies UC-affected individuals at 5-fold elevated risk of colectomy with data from the UK Biobank population cohort studies, independently replicated in an NIDDK-IBDGC dataset. Prediction of gene expression from relatively small transcriptome datasets can thus be used in conjunction with TWASs for stratification of risk of disease complications. [ABSTRACT FROM AUTHOR]